Citation: Brian W. Miller, Leonardo Frid, Tony Chang, Nathan Piekielek, Andrew J. Hansen, Jeffrey T. Morisette. Combining state-and-transition simulations and species distribution models to anticipate the effects of climate change[J]. AIMS Environmental Science, 2015, 2(2): 400-426. doi: 10.3934/environsci.2015.2.400
[1] | Oltion Marko, Joana Gjipalaj, Dritan Profka, Neritan Shkodrani . Soil erosion estimation using Erosion Potential Method in the Vjosa River Basin, Albania. AIMS Environmental Science, 2023, 10(1): 191-205. doi: 10.3934/environsci.2023011 |
[2] | Alma Sobrino-Figueroa, Sergio H. Álvarez Hernandez, Carlos Álvarez Silva C . Evaluation of the freshwater copepod Acanthocyclops americanus (Marsh, 1983) (Cyclopidae) response to Cd, Cr, Cu, Hg, Mn, Ni and Pb. AIMS Environmental Science, 2020, 7(6): 449-463. doi: 10.3934/environsci.2020029 |
[3] | Raden Darmawan, Sri Rachmania Juliastuti, Nuniek Hendrianie, Orchidea Rachmaniah, Nadila Shafira Kusnadi, Ghassani Salsabila Ramadhani, Yawo Serge Marcel, Simpliste Dusabe, Masato Tominaga . Effect of electrode modification on the production of electrical energy and degradation of Cr (Ⅵ) waste using tubular microbial fuel cell. AIMS Environmental Science, 2022, 9(4): 505-525. doi: 10.3934/environsci.2022030 |
[4] | Seiran Haghgoo, Jamil Amanollahi, Barzan Bahrami Kamangar, Shahryar Sorooshian . Decision models enhancing environmental flow sustainability: A strategic approach to water resource management. AIMS Environmental Science, 2024, 11(6): 900-917. doi: 10.3934/environsci.2024045 |
[5] | M.A. Rahim, M.G. Mostafa . Impact of sugar mills effluent on environment around mills area. AIMS Environmental Science, 2021, 8(1): 86-99. doi: 10.3934/environsci.2021006 |
[6] | Motharasan Manogaran, Mohd Izuan Effendi Halmi, Ahmad Razi Othman, Nur Adeela Yasid, Baskaran Gunasekaran, Mohd Yunus Abd Shukor . Decolorization of Reactive Red 120 by a novel bacterial consortium: Kinetics and heavy metal inhibition study. AIMS Environmental Science, 2023, 10(3): 424-445. doi: 10.3934/environsci.2023024 |
[7] | Martina Grifoni, Francesca Pedron, Gianniantonio Petruzzelli, Irene Rosellini, Meri Barbafieri, Elisabetta Franchi, Roberto Bagatin . Assessment of repeated harvests on mercury and arsenic phytoextraction in a multi-contaminated industrial soil. AIMS Environmental Science, 2017, 4(2): 187-205. doi: 10.3934/environsci.2017.2.187 |
[8] | Adrian Schmid-Breton . Transboundary flood risk management in the Rhine river basin. AIMS Environmental Science, 2016, 3(4): 871-888. doi: 10.3934/environsci.2016.4.871 |
[9] | Jerry R. Miller . Potential ecological impacts of trace metals on aquatic biota within the Upper Little Tennessee River Basin, North Carolina. AIMS Environmental Science, 2016, 3(3): 305-325. doi: 10.3934/environsci.2016.3.305 |
[10] | Maja Radziemska, Agnieszka Bęś, Zygmunt M. Gusiatin, Jerzy Jeznach, Zbigniew Mazur, Martin Brtnický . Novel combined amendments for sustainable remediation of the Pb-contaminated soil. AIMS Environmental Science, 2020, 7(1): 1-12. doi: 10.3934/environsci.2020001 |
Asopos River basin, in East-Central Greece is characterized by a recorded problem of hexavalent chromium contamination, exceeding in some cases the value of 100 μg L−1 as measured in groundwater samples collected from the area [1]. Geogenic and anthropogenic components have contributed to the recorded high levels of chromium contamination in the Asopos River Basin. The geological character of surrounding area of Asopos River basin mainly is Neogene lake-shallow marine sediments, clastic formations of continental origin and parts of ophiolite complexes [2]. The detection of elements, such as Cr and Ni in soils and waters, has often a strong lithogenic origin correlated to the existence of ophiolite outcrops composed by ultramafic rocks [3], but also Fe-Ni deposits [4,5]. Cases where occurrence of hexavalent chromium is primarily of geogenic origin have also been documented for California [6,7,8], New Caledonia [9], Zimbabwe [10], Italy [11], etc. The geogenic mobilization of Cr(Ⅵ) from highly insoluble Cr(Ⅲ) minerals, like chromite, takes place via a two-stage mechanism [10]. At first Cr(Ⅲ) in the matrix of chromite is hydrolyzed to Cr(OH)3. The following stage is the oxidation of Cr(Ⅲ) to Cr(Ⅵ) under the action of easily reducible Mn oxides (the mixed Mn(Ⅱ)/Mn(Ⅲ) oxide hausmannite (Mn3O4) or the Mn(Ⅲ) oxide manganite (MnOOH)). It is considered [10] that this natural process is probably continuous in concretionary subsoils subject to wetting-drying cycles.
Industrialization in the Asopos River Basin area started in the early 1960’s and today more than 400 installations exist in the area. Metal finishing and manufacturing plants, often using Cr-based chemicals in their processes were major chromium polluters in the area. All facilities were obliged to treat their effluents in-house in appropriate wastewater treatment units, but until 2008 the treated effluent was allowed to be discharged underground via disposal in absorption type sinks. As a result of this the anthropogenic factor, the observed Cr contamination in Asopos River basin cannot be neglected. University of Athens research [12] suggests that the Asopos river sediments are enriched with Cr and Ni by a factor of almost 2.5 compared to the local background values.
The objective of this work was to:
investigate whether previous disposal practices in four (4) metal finishing facilities have led to potential contamination to the adjacent soils, and
to compare the potential contaminated soil concentration values of the metal finishing facilities with
❖ greater area background metal concentration values
❖ Potentially polluted and newly investigated soil metal concentration values of the Inofyta Industrial Area (IIA).
The four investigated metal finishing industrial sites are Hellenic Aerospace Industry S.A. (designated as HAI), Europa Profile Aluminium S.A. (EU), Aluminco S.A. (AL) and Viometale S.A. (Ⅵ) [13,14,15,16]. The reason for selecting these metal finishing sites is:
HAI, EU, AL are three of the larger installations in the area using Hexavalent Chromium
In Ⅵ a discrete thin metal contamination layer on surface soils was found to the south of the area during a Prefecture Environmental Audit.
Prefecture of Sterea Ellada(relevant Environmental Authority) considers HAI, EU, AL and VI as priority potential polluters.
The study area is focusing on the four above mentioned metal finishing industrial sites HAI, EU, AL VI and the Inofyta Industrial Area (IIA). Sampling strategy involved the collection of three groups of soil samples and for comparison reasons the data collected from Inofyta industrial area by the research team of the EU funded project LIFE-CHARM “Chromium in Asopos groundwater system: Remediation technologies and Measures” [17] (data available at http://www.charm-life.gr/charm/index.php/en/documents) were also evaluated, called as fourth group (LGR-4) sampling. The first group (GR-1) assumed free of anthropogenic influence was intended to represent natural geochemical background values close to the industrial sites. Selected sampling points (depicted as HR, ER, AR and VR in Figure 1) were in the vicinity of the metal finishing and industrial sites but not affected from any potential polluting factors. The second group (GR-2), collected in the period 2011-2012 (campaigns by Sybilla Ltd), and consisted of samples from areas suspected of pollution from ongoing activities or historical disposal practices. Samples are either samples from soil shallow layers, i.e. 0-80 cm, or soil core samples from boreholes, up to a depth of 15 meters. The greater Asopos river Basin area Industrial Sites, and location of investigated surface soil sampling points of investigated metal finishing units of GR-1 (HR1-VR1-ER1-ER2-ER3-ER4-ER5-ER6, AR1.) and GR-2 (HB1-HB2-HB3, VB2-VB2, EB1-EB2-EB3-EB4, AB1-AB2-AB3-AB4, HR1-VR1-ER1-ER2-ER3-ER4-ER5-ER6, AR1) group campaigns are presented in Figure 1. GR-1, GR-2, GR-3 and LGR-4 sampling locations and the total number of analyzed samples per industrial site are presented in Tables 1-4.
The third group (GR-3) consisted of more than 10 samples collected in summer 2015 (campaigns by Sybilla ltd in the framework of EU IED Directive [18]) Baseline Site Investigation Study [19,20]) from areas suspected of pollution from ongoing activities or previous disposal practices. Samples are either soils collected from the shallow layers, i.e. 0-80 cm, or core samples from boreholes, drilled down to a depth of about 15 meters. The location of boreholes is shown in Figure 2.
For comparison reasons data collected from Inofyta industrial area by the research team of the EU funded project LIFE-CHARM “Chromium in Asopos groundwater system: Remediation technologies and Measures” [17] (data available at http://www.charm-life.gr/charm/index.php/en/documents) were also evaluated. This Life Project, fourth group campaign (LGR-4) of samples was collected at the period 2011-2012. A sampling program was carried out during which seven (7) new groundwater wells with a depth of approximately 30-50 m were drilled at Inofyta industrial area (N1, N2, N3, N4, N5, N6, N7), between January and February 2012. During the construction, drill core samples were collected from each borehole and relevant chemical analyses followed. Boreholes sampling was followed by a surface soil sampling where a series of 12 surface soil samples were also collected during this action in order to investigate the presence of Cr.The location of relevant boreholes is shown in Figure 3.
Laboratories involved in the chemical analysis of the collected soil samples, the analytical methods used, and the parameters analyzed, are presented in Tables 1-4.
Site | No of sampling locations (no of samples) | Parameters analyzed | Methods | Labs (*) |
HAI | 1 (1) | Cr, Ni, Cu, Zn, Pb, Al | Digestion with AR(a) | Andreou |
Cr(Ⅵ) | Elution with water(b) | |||
Europa | 6 (6) | Cr, Ni | Digestion with AR(a) | Andreou |
Cr(Ⅵ) | Elution with water(b) | |||
Aluminco | 1 (1) | Cr, Ni, Fe, Al | Digestion with AR(a)XRF(d) | EuF/LabMet |
Cr(Ⅵ) | Alkaline digestion(c) | LabMet | ||
Viometale | 1 (1) | Cr, Ni, Cu, Zn, etc.Cr(Ⅵ) | XRF(d), AR(a)Alkaline digestion(c) | LabMet |
Site | No of sampling locations (no of samples) | Parameters analyzed | Methods | Labs (*) | |
Un-contaminated | Suspected for contamination | ||||
HAI | 1 (7) | 3 (42) | Cr, Ni, Cu, Zn, Pb, Al | Digestion with AR(a) | Andreou |
Cr(Ⅵ) | Elution with water(b) | ||||
Europa | 6 (13) | 4 (49) | Cr, Ni | Digestion with AR(a) | Andreou |
Cr(Ⅵ) | Elution with water(b) | ||||
Aluminco | 1 (6) | 4 (12) | Cr, Ni, Fe, Al | Digestion with AR(a)XRF(d) | EuF/LabMet |
Cr(Ⅵ) | Alkaline digestion(c) | LabMet | |||
Viometale | 1 (4) | 6 (19) | Cr, Ni, Cu, Zn, etc. | XRF(d)AR(a) | LabMet |
Cr(Ⅵ) | Alkaline digestion(c) |
Site | No of sampling locations (no of samples) | Parameters analyzed | Methods | Labs (*) | |
Un-contaminated | Suspected for contamination | ||||
Europa | 2 | 3 | Cr, Ni, Fe, Al | Digestion with AR(a) | LabMet |
Cr(Ⅵ) | Alkaline digestion(c) | ||||
Aluminco | 1 | 5 | Cr, Ni, Fe, Al | Digestion with AR(a) | LabMet |
Cr(Ⅵ) | Alkaline digestion(c) |
Site | No of sampling locations (no of samples) | Parameters analyzed |
Methods | Labs(*) | |
Un-contaminated | Suspected for contamination | ||||
Boreholes | - | 38 | Cr, Ni, Fe, Al | XRF(d) | LabMet |
Cr(Ⅵ) | AR(a) | ||||
Surface Soil | - | 12 | Cr, Ni, Fe, Al | XRF(d) | LabMet |
Cr(Ⅵ) | AR(a) | ||||
(a) Digestion with aqua regia followed by determination of metals in solution by AAS or ICP-MS (EN 13657) (b) Elution with water, determination of soluble Cr(Ⅵ) (DIN 38405-24: 05.87, AWWA-3500-Cr/B) (c) Alkaline digestion, determination of extracted Cr(Ⅵ) (USEPA, SW-846 Methods 3060A and 7196) (d) Determination of total elements concentration by X-ray fluorescence spectrometry (EN 15309) (*) Laboratories: (a) Andreou, K. Andreou. Ltd, Athens, (b) EuF: Eurofins Umwelt Ost GmbH, Jena, Germany, (c) LabMet: Laboratory of Metallurgy, NTUA, Athens. For the majority of samples, namely those collected from HAI, Europa and Aluminco, the elemental analysis was carried out following the digestion of samples with aqua regia (AR). The samples collected from Viometale were analyzed by X-ray fluorescence (XRF) spectrometry, (mainly due to time constraints - XRF analysis is much more rapid, as there is no need for any pretreatment steps, such as acid leaching or fusion). The LIFE-CHARM samples were also analyzed by XRF. |
Analysis by XRF determines the total content of elements in the solid samples, which does not coincide with the amount extracted by aqua regia. As discussed in a previous paper [21], using GR-1 and GR-2 campaigns results and a dataset of 40 surface soil samples collected throughout the whole Greek territory in the framework of the Geochemical Atlas of Europe [22], the concentration of chromium determined by the method of aqua regia digestion, Cr (AR), is about 4 times less compared to the total content of Cr determined by XRF, Cr (XRF). This can be attributed to the fact that the highest percentage of chromium in Greek soils is incorporated into insoluble minerals, e.g. substituted aluminosilicates or spinel minerals like chromite, which are not affected by the AR digestion. On the contrary, the total amount of Ni in soils is soluble in AR. As a consequence, the concentration of Ni determined by the AR digestion method, Ni(AR), is very close to the total content, as determined by XRF, Ni (XRF).
For the assessment of Cr and Ni background concentration values in Asopos River Basin area soils, we quote data collected from various references consisting of various soil samples analyzed from locations which were assumed free of contamination from industrial activities. Total Cr, Ni and Cr (Ⅵ) concentrations are presented in the following Table 5.
Area (Number of samples) |
Cr (mg/kg) |
Ni (mg/kg) |
Cr(Ⅵ) (mg/kg) |
Source | ||
Range | Mean | Range | Mean | Range | ||
Asopos (n = 30) |
60-410 | 220 | 91-1200 | 620 | >0.1-9.3 (a) | [21] |
Oropos (n = 33) |
17-600 | 212 | -- | [3] | ||
Thebes (n = 51) |
134-856 | 277 | 621-2639 | 1591 | -- | [23] |
Atalante (n = 64) |
48-4200 | 453 | 44-2730 | 533 | -- | [24] |
All Greece (n = 41) |
2-466 | 102 | 2-1812 | 171 | -- | [22] |
(a) Cr(Ⅵ) detected in 3 among the 30 analyzed reference soils (5.5, 6.0 and 9.3 mg/kg). |
Chromium concentration values determined by aqua regia (AR) method are similar to the concentration levels determined at Oropos [3] and at Thebes [24], adjacent to the study area, with similar geological formations. As far as Cr (Ⅵ) is concerned, this species was detected only in three (3) among the 26 analyzed samples of Asopos Area, with concentrations 5.5, 6.0 and 9.3 mg/kg respectively. Ni concentrations determined by aqua regia (AR) method in the Asopos river Basin Area, were significantly higher.
Since Greece has not yet developed national soil quality guidelines for Cr and Ni, relevant guidelines from three European countries, namely Italy, Germany and Belgium (Wallonia), were used and are presented in Table 6 [25]. These values represent the upper allowed concentration levels of Cr and Ni in soils for residential and industrial land use. Cr(Ⅵ) threshold concentrations limits exist only in the regulations of Wallonia.
As seen in Table 5, the mean concentration of total Cr in Asopos soils (220 mg/kg), slightly exceeds the Italian threshold limit value for residential areas, but satisfies all other limit values. On the contrary, the mean concentration of Ni (620 mg/kg) exceeds the German limit for industrial areas. In Thebes’s soils, most samples of Ni were exceeding the threshold limits. An analysis of Table 6, ends up to the conclusion that the use of these soil quality guidelines when applied in metalliferous areas, like those encountered in many regions of Greece is questionable (since the geochemical background for at least Cr and Ni elements is often higher).
Soil limit values (mg/kg) | ||||||
Residential areas* | Industrial areas* | |||||
IT | DE | BE(W) | IT | DE | BE(W) | |
Cr | 150 | 400 | 520 | 800 | 1000 | 700 |
Ni | 120 | 140 | 300 | 500 | 900 | 500 |
Cr(Ⅵ) | -- | -- | 4.2 | -- | -- | -- |
(*) Cr, Ni digestion of samples with aqua regia (AR). |
Figure 1b presents three boreholes that were drilled at HAI grounds, close to potentially polluting HAI sites, i.e. ponds that were used for the treatment of hazardous industrial wastewater, storage of physicochemically treated wastewater and for the storage and drying of industrial sludge. Total Cr concentration profiles are presented in Figure 4a. Total Chromium, Aqua regia (AR) Cr(AR), concentration values varied from 51 to 281 mg/kg with a mean value of 124 mg/kg. Total Ni aqua regia (AR) Ni (AR), concentration values varied from 132 to 618 mg/kg with a mean value of 262 mg/kg (Figure 5a). All these Cr(AR) and Ni(AR) soil concentration values are of the same order of magnitude with soil concentration values assumed free of anthropogenic contamination. Hexavalent Cr content was not detected in any of the soil samples examined.
For assessing the potential soil investigation in Europa grounds four boreholes were drilled at locations shown in Figure 1d. Borehole #1 (EB1) was drilled beneath the sink, where physicochemically treated wastewater from the electrostatic coating process was disposed for over two decades. For assessing the potentially contaminated soil directly beneath the sink, one inclined borehole was drilled with a 45o angle (EB4). A schematic drawing of the two above mentioned boreholes is given in Figure 6.
Borehole EB3 was drilled at a location where a small stream enters the metal finishing Europa installation area whereas borehole EB2 was drilled 60 meters downstream of the treated wastewaters sink. Total Cr concentration values of Europa boreholes are within the background values of the area as presented within this article, except for two soil samples of EB1 (depths of 8 and 11 m), where Cr soil concentration values are 619 and 849 mg/kg respectively. Borehole EB4 (inclined borehole) close to EB1, presents a similar total Cr profile but the Cr soil concentration values at depths of 8 and 11 m lie within the background levels range. It must be noticed that at the bottom of the sink (depth of 4 m) a thin greenish solid layer no more than a few centimeters thickness was found. This layer was a Cr-rich sludge, i.e. Cr concentration value of about 80100 mg/kg of trivalent Cr. It is argued that the treated effluents containing some small amount of suspended sludge solids, had been filtered and retained inside the sink. It must be noticed though that the soil beneath the sink is relatively contamination free. Soil samples at 4, 5 and 6 meters under the sink (boreholes EB1 and EB4) have low Cr concentration values, ranging from 100 to 280 mg/kg. Ni soil concentration values varied between 257 and 1080 mg/kg and were close to Ni concentration values in reference soils (Figure 5b). Hexavalent chromium was found in 15 of the 49 samples, at a maximum soil concentration value of 10.1 mg/kg, close to reference soil samples.
Five surface soil samples were analyzed during the Fourth Group GR-4 campaign at Europa Profile Aluminium grounds, within the facility borders. Chromium concentration values are shown in Figure 7. Chromium, Cr (AR), concentration varied between 95 and 495 mg/kg with a mean value of 247 mg/kg. All these values but one are within the range of concentrations measured in the uncontaminated reference soils. Hexavalent Cr (Ⅳ) soil concentration values were below detection limit in any of these samples examined.
The metal finishing unit Aluminco disposed its physicochemicaly treated industrial wastewater in two parallel sinks, at a depth of 4 m, from 2003 until May 2008. For the assessment of potentially contaminated soil four boreholes, inclined with slopes 60o-75o, were drilled (two boreholes per sink), as shown in Figure 1e. There exists a thin layer of polluted soil just at the bottom of the sinks that seems to be affected due to the entrainment of suspended solids. Soil samples were collected below the sinks, from depths 4 m to about 10 m. Soil concentration values of total Cr profiles are shown in Figure 4c. At depth of 4 m, boreholes AB4 and AB3 soil samples have high total Cr (AR) concentration values between 710 mg/kg and 2010 mg/kg, while borehole AB2 Cr soil concentration value was slightly above the reference range. It appears that the treated effluents disposal has an impact on a limited depth soil layer below the sink since the samples of depths at 5, 6 and 9 meters had soil Cr concentration values from 200 to 380 mg/kg. Soil concentration values of Hexavalent chromium (analyzed with the alkaline digestion method) was found to be from 0.2 to 4 mg/kg. Soil concentration values of Ni, ranged from 830 to1650 mg/kg, as presented in Figure 5c. The two Ni highest soil concentration values 1500 and 1650 mg/kg exceed the range of soil concentration values measured in Asopos river Basin reference soils, while similar levels of Ni soil concentration values were measured in soils assumed free of contamination near Thebes and thus a geogenic origin cannot be excluded [23].
Ten surface soil samples were analyzed during Fourth Group (GR-4) campaign at Aluminco grounds, and their locations are presented in Figure 2, within the facility borders. Chromium soil concentration values are shown in Figure 8. Chromium, Cr (AR), concentration varied between 108 and 327 mg/kg with a mean value of 197 mg/kg. All these values are within the range of concentrations measured in the uncontaminated reference soils. Hexavalent Cr (Ⅳ) soil concentration values were below detection limit in any of these samples examined.
As mentioned before in Viometale site a discrete thin metal contamination layer on surface soils was found to the south of the area during a Prefecture Environmental Audit. Two boreholes were drilled in metal finishing unit Viometale grounds for assessing potentially contaminated soils. These boreholes, denoted as VB1 and VB2, were drilled near and beneath a sink and shown in Figure 1c. Figure 1c, depicts the surface (and low depth (0-0.8m)) soil samples noted as VS1, VS2, VS3 and VS4 samples. Samples VS1 to VS4 are assumed to be representative of incoming pollution located at a land point receiving runoff water from nearby fields, and the outlet of a duct, draining storm water situated to the north of the national road. Boreholes VB1 and VB2 soil Cr concentration values and profiles are depicted in Figure 4d. Since the metal finishing unit at Viometale does not use any trivalent or hexavalent Cr-based chemicals, Cr soil concentrations were measured for comparison reasons, while pollutant of concern (POC) related to these industrial operations is mainly Ni. Measured Cr(XRF) soil concentration values varied from 234 to 2950 mg/kg, equivalent to soil concentration values 58-738 mg/kg of Cr (AR), since Cr(XRF) soil concentration values are approximately 4 times higher to Cr(AR) values. Soil concentrations of Ni remained within the range of reference soils, as shown in Figure 5d and varied from 230 to 1064 mg/kg. Soil surface and low depth samples at points VS1, VS2, VS3 and VS4, end up to the conclusion that there exists high Ni soil contamination mainly in the upper 40 cm soil layer, measured Ni soil concentration values up to 10340 mg/kg.
As depicted in Figure 9, Inofyta Industrial Area (IIA) investigationcampaign consisted of samples collected at the period 2011-2012 (LGR-4 campaigns by EU funded project LIFE-CHARM) [17], where
drill core samples were collected from seven boreholes (N1, N2, N3, N4, N5, N6, N7)
a series of 12 surface soil samples were also collected
and relevant chemical analyses followed in order to investigate the presence of Cr.
The relevant soil Cr (XRF)/Cr (AR) concentration values are presented at Figures 9 and 10. The elevated Cr concentrations demonstrate that the industrial site studied is contaminated. Chromium, Cr (AR), concentration values varied between 51 and 281 mg/kg with a mean value of 430 mg/kg which exceeds slightly the maximum background concentration. 26 of these values are within the range of concentrations measured in the uncontaminated reference soils while 12 exceed the range of background concentrations. Hexavalent Cr (Ⅳ) soil concentration values were below detection limit in of 37 (out of 38) samples examined. Potential sources of this contamination are either buried hazardous wastes or wastewater directly discharged into groundwater.
Soil samples collected close to the existing wastewater physicochemical treatment unit and the sludge storage facilities at the Hellenic AerospaceIndustry S.A. (HAI) do not seem to indicate soil contamination.
At the second metal finishing unit under investigation, Europa (EU), disposal of physicochemicaly treated effluents in absorption type sinks led to a thin layer of sludge solids in the bottom of the sink. However, the soil beneath the disposal sink was found to be rather contamination free, and the relevant contamination seems to be localized and not dispersed further.
Measured soil concentration values at Aluminco (AL) end up to the conclusion that there exists a thin layer of polluted soil just at the bottom of the sinks that seems to be affected due to the entrainment of suspended solids. At lower soil layers the soil concentration values of Cr and Ni varied within the range of reference soils concentration values. So the above mentioned contamination (polluted soil just at the bottom of the sinks), does not seem to have an impact on soil contamination beneath it.
Finally, at the last unit analyzed for soil pollution, Viometale (Ⅵ), Ni contamination of surface soils was found to the south of the area. However, the soil near and beneath the sink used in the past for physicochemicaly treated effluents disposal, seems to be contamination free. So the above mentioned contamination (thin discrete layer on the surface soil), does not seem to have an impact on soil contamination beneath it.
Comparison of soil pollution measurement of the four above mentioned metal finishing units (HAI, EU, AL, VI) with a group of data collected in the framework of EU funded project LIFE-CHARM indicate that the Inofyta Industrial Area (IIA) soil seems to be contaminated. Mean Cr soil concentration values of the boreholes samples slightly exceed the maximum background concentration of the greater area while around 33% of the IIA Cr soil concentration values are well above the maximum background concentration by a number of two.
Comparison of obtained results allows to ascertain that previous disposal practices at the mentioned four (4) metal finishing facilities HAI, EU, AL and VI have not led to significant potential contamination to the adjacent soils and definitely these installation do not pose a general soil contamination threat to the study area. There was no indication of downstream migration from the land-based treated effluents disposal of the above mentioned facilities. Soil concentration values adjacent to these facilities were rather free of contamination.
The Cr and Ni soil concentration values in the lower soil layers of the above mentioned metal finishing facilities are of the same order of magnitude with greater area background metal concentration values and significantly lower of documented and newly investigated contaminated soil metal concentration values of the Inofyta Industrial Area (IIA).
At Inofyta Industrial Area (IIA) the detected soil contamination (measured high Cr soil concentration values) requires special attention for future environmental protection planning actions.
The authors declare there is no conflict of interest.
[1] | Miller BW, Morisette JT (2014) Integrating research tools to support resource management under climate change. Ecol Soc 19: 41. |
[2] |
Guisan A, Thuiller W (2005) Predicting species distribution: Offering more than simple habitat models. Ecol Lett 8: 993-1009. doi: 10.1111/j.1461-0248.2005.00792.x
![]() |
[3] | Sinclair SJ, White MD, Newell GR (2010) How useful are species distribution models for managing biodiversity under future climates? Ecol Soc 15: 8. |
[4] | Daniel CJ, Frid L (2012) Predicting landscape vegetation dynamics using state-and-transition simulation models, In: Kerns BK, Shlisky AJ, Daniel CJ, technical editors, Proc First Landscape State-and-Transition Sim Model Conf, June 14–16, 2011, Portland, OR, Gen Tech Rep PNW-GTR-869, Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 5-22. |
[5] | Franklin J (2009) Mapping species distributions: Spatial inference and prediction. Cambridge, UK: Cambridge University Press. |
[6] |
Franklin J (2013) Species distribution models in conservation biogeography: Developments and challenges. Div Distr 19: 1217-1223. doi: 10.1111/ddi.12125
![]() |
[7] |
Dobrowski SZ, Thorne JH, Greenberg JA, et al. (2011) Modeling plant ranges over 75 years of climate change in California, USA: Temporal transferability and species traits. Ecol Monogr 81:241-257. doi: 10.1890/10-1325.1
![]() |
[8] |
Bell DM, Bradford JB, Lauenroth WK (2014) Early indicators of change: Divergent climate envelopes between tree life stages imply range shifts in the western United States. Global Ecol Biogeog 23: 168-180. doi: 10.1111/geb.12109
![]() |
[9] |
Stephenson NL (1998) Actual evapotranspiration and deficit: Biologically meaningful correlates of vegetation distribution across spatial scales. J Biogeogr 25: 855-870. doi: 10.1046/j.1365-2699.1998.00233.x
![]() |
[10] |
Benito-Garzón M, Ha-Duong M, Frascaria-Lacoste N, et al. (2013) Habitat restoration and climate change: Dealing with climate variability, incomplete data, and management decisions with tree translocations. Restor Ecol 21: 530-536. doi: 10.1111/rec.12032
![]() |
[11] |
Schwartz MW (2012) Using niche models with climate projections to inform conservation management decisions. Biol Cons 155: 149-156. doi: 10.1016/j.biocon.2012.06.011
![]() |
[12] |
Iverson LR, Prasad AM, Matthews SN, et al. (2011) Lessons learned while integrating habitat, dispersal, disturbance, and life-history traits into species habitat models under climate change. Ecosystems 14: 1005-1020. doi: 10.1007/s10021-011-9456-4
![]() |
[13] |
Westoby M, Walker B, Noy-Meir I (1989) Opportunistic management for rangelands not at equilibrium. J Range Manage 42: 266-274. doi: 10.2307/3899492
![]() |
[14] |
Bestelmeyer BT, Brown JR, Havstad KM, et al. (2003) Development and use of state-and-transition models for rangelands. J Range Manage 56: 114-126. doi: 10.2307/4003894
![]() |
[15] | Stringham TK, Krueger WC, Shaver PL (2001) States, transitions, and thresholds: Further refinement for rangeland applications. Special Report Number 1024, Oregon State University Agricultural Experiment Station: 1-15. |
[16] |
Parker DC, Manson SM, Janssen MA, et al. (2003) Multi-agent systems for the simulation of land-use and land-cover change: A review. Ann Assoc American Geogr 93: 314-337. doi: 10.1111/1467-8306.9302004
![]() |
[17] | Li H, Reynolds JF (1997) Modeling effects of spatial pattern, drought, and grazing on rates of rangeland degradation: A combined Markov and cellular automaton approach, In: Quattrochi DA, Goodchild MF, editors, Scale in Remote Sensing and GIS, Boca Raton, FL: Lewis Publishers,211-230. |
[18] |
Balzter H, Braun PW, Köhler W (1998) Cellular automata models for vegetation dynamics. Ecol Model 107: 113-125. doi: 10.1016/S0304-3800(97)00202-0
![]() |
[19] |
Bestelmeyer BT, Goolsby DP, Archer SR (2011) Spatial perspectives in state-and-transition models: A missing link to land management? J Appl Ecol 48: 746-757. doi: 10.1111/j.1365-2664.2011.01982.x
![]() |
[20] |
Forbis TA, Provencher L, Frid L, et al. (2006) Great Basin land management planning using ecological modeling. Environ Manage 38: 62-83. doi: 10.1007/s00267-005-0089-2
![]() |
[21] |
Provencher L, Forbis TA, Frid L, et al. (2007) Comparing alternative management strategies of fire, grazing, and weed control using spatial modeling. Ecol Model 209: 249-263. doi: 10.1016/j.ecolmodel.2007.06.030
![]() |
[22] |
Frid L, Wilmshurst JF (2009) Decision analysis to evaluate control strategies for Crested Wheatgrass (Agropyron cristatum) in Grasslands National Park of Canada. Invasive Plant Sci Manage 2: 324-336. doi: 10.1614/IPSM-09-006.1
![]() |
[23] |
Costanza JK, Hulcr J, Koch FH, et al. (2012) Simulating the effects of the southern pine beetle on regional dynamics 60 years into the future. Ecol Model 244: 93-103. doi: 10.1016/j.ecolmodel.2012.06.037
![]() |
[24] |
Keane RE, Holsinger LM, Parsons RA, et al. (2008) Climate change effects on historical range and variability of two large landscapes in western Montana, USA. Forest Ecol Manage 254:375-389. doi: 10.1016/j.foreco.2007.08.013
![]() |
[25] |
Strand EK, Vierling LA, Bunting SC, et al. (2009) Quantifying successional rates in western aspen woodlands: Current conditions, future predictions. Forest Ecol Manage 257: 1705-1715. doi: 10.1016/j.foreco.2009.01.026
![]() |
[26] |
Halofsky JE, Hemstrom MA, Conklin DR, et al. (2013) Assessing potential climate change effects on vegetation using a linked model approach. Ecol Model 266: 131-143. doi: 10.1016/j.ecolmodel.2013.07.003
![]() |
[27] | Hemstrom MA, Halofsky JE, Conklin DR, et al. (2014) Developing climate-informed state-and-transition models, In: Halofsky JE, Creutzburg MK, Hemstrom MA, editors, Integrating Social, Economic, and Ecological Values Across Large Landscapes, Gen Tech Rep PNW-GTR-896, Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 175-202. |
[28] | Yospin GI, Bridgham SD, Neilson RP, et al. (2014) A new model to simulate climate change impacts on forest succession for local land management. Ecol Appl 25:226-242. |
[29] |
Chang T, Hansen AJ, Piekielek N (2014) Patterns and variability of projected bioclimatic habitat for Pinus albicaulis in the Greater Yellowstone Area. PLoS ONE 9: e111669. doi: 10.1371/journal.pone.0111669
![]() |
[30] |
Whitlock C (1993) Postglacial vegetation and climate of Grand Teton and southern Yellowstone National Parks. Ecol Monogr 63: 173-198. doi: 10.2307/2937179
![]() |
[31] | Greater Yellowstone Coordinating Committee Whitebark Pine Subcommittee (2011) Whitebark Pine Strategy for the Greater Yellowstone Area. US Dept of Agriculture, Forest Service, Forest Health Protection, and Grand Teton National Park. |
[32] | Gibson K, Skov K, Kegley S, et al. (2008) Mountain pine beetle impacts in high-elevation five-needle pines: current trends and challenges. US Department of Agriculture, Forest Service, Forest Health Protection. |
[33] | Larson ER, Kipfmueller KF (2012) Ecological disaster or the limits of observation? Reconciling modern declines with the long-term dynamics of whitebark pine communities. Geogr Compass 6:189-214. |
[34] |
Romme WH, Turner MG (1991) Implications of global climate change for biogeographic patterns in the Greater Yellowstone Ecosystem. Conserv Biol 5: 373-386. doi: 10.1111/j.1523-1739.1991.tb00151.x
![]() |
[35] |
Bartlein PJ, Whitlock C, Shafer SL (1997) Future climate in the Yellowstone national park region and its potential impact on vegetation. Conserv Biol 11: 782-792. doi: 10.1046/j.1523-1739.1997.95383.x
![]() |
[36] | Schrag AM, Bunn AG, Graumlich LJ (2008) Influence of bioclimatic variables on tree‐line conifer distribution in the Greater Yellowstone Ecosystem: Implications for species of conservation concern. J Biogeogr 35(4): 698-710. |
[37] | U.S. Fish and Wildlife Service (2011) Whitebark pine to be designated a candidate for endangered species protection. Available from: http://www.fws.gov/Wyominges/Pages/Species/Findings/2011_WBP.html |
[38] |
Freire J (2012) Making computations and publications reproducible with VisTrails. Comput Sci Eng 14: 18-25. doi: 10.1109/MCSE.2012.76
![]() |
[39] |
Morisette JT, Jarnevich CS, Holcombe TR, et al. (2013) VisTrails SAHM: Visualization and workflow management for species habitat modeling. Ecography 36: 129-135. doi: 10.1111/j.1600-0587.2012.07815.x
![]() |
[40] | Lockman IB, DeNitto GA, Courter A, et al. (2007) WLIS: The whitebarklimber pine information system and what it can do for you. In: Proceedings of the Conference Whitebark Pine: A Pacific Coast Perspective. Ashland, OR: US Department of Agriculture, Forest Service, Pacific Northwest Region, 146-147. |
[41] | Greater Yellowstone Whitebark Pine Monitoring Working Group (2011) Interagency Whitebark Pine Monitoring Protocol for the Greater Yellowstone Ecosystem, Version 1.1. Greater Yellowstone Coordinating Committee, Bozeman, MT. Available from: http://fedgycc.org/documents/GYE.WBP.MonitoringProtocol.V1.1June2011.pdf |
[42] |
Smith WB (2002) FIA: Forest inventory and analysis: a national inventory and monitoring program. Environ Pollut 116: S233-S242. doi: 10.1016/S0269-7491(01)00255-X
![]() |
[43] |
Gibson J, Moisen G, Frescino T, et al. (2014) Using publicly available forest inventory data in climate-based models of tree species distribution: Examining effects of true versus altered location coordinates. Ecosystems 17: 43-53. doi: 10.1007/s10021-013-9703-y
![]() |
[44] |
Drake BG, Gonzàlez-Meler MA, Long SP (1997) More efficient plants: A consequence of rising atmospheric CO2? Annu Rev Plant Biol 48: 609-639. doi: 10.1146/annurev.arplant.48.1.609
![]() |
[45] |
Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: A misleading measure of the performance of predictive distribution models. Global Ecol Biogeogr 17: 145-151. doi: 10.1111/j.1466-8238.2007.00358.x
![]() |
[46] | Voldoire A, Sanchez-Gomez E, Salas y Mélia, et al. (2013) The CNRM-CM5. 1 global climate model: Description and basic evaluation. Clim Dynam 40: 2091-2121. |
[47] |
Martin GM, Bellouin N, Collins WJ, et al. (2011) The HadGEM2 family of met office unified model climate configurations. Geosci Model Dev 4: 723-757. doi: 10.5194/gmd-4-723-2011
![]() |
[48] |
Deser C, Phillips AS, Alexander MA, et al. (2014) Projecting North American climate over the next 50 years: Uncertainty due to internal variability. J Climate 27: 2271-2296. doi: 10.1175/JCLI-D-13-00451.1
![]() |
[49] | Apex Resource Management Solutions (2014) ST-Sim state-and-transition simulation model software. Available from: www.apexrms.com/stsm |
[50] |
Rollins MG (2009) LANDFIRE: A nationally consistent vegetation, wildland fire, and fuel assessment. Int J Wildland Fire 18: 235-249. doi: 10.1071/WF08088
![]() |
[51] |
Landenburger L, Lawrence RL, Podruzny S, et al. (2008) Mapping regional distribution of a single tree species: Whitebark Pine in the Greater Yellowstone Ecosystem. Sensors 8: 4983-4994. doi: 10.3390/s8084983
![]() |
[52] |
Aukema BH, Carroll AL, Zhu J, et al. (2006) Landscape level analysis of mountain pine beetle in British Columbia, Canada: Spatiotemporal development and spatial synchrony within the present outbreak. Ecography 29: 427-441. doi: 10.1111/j.2006.0906-7590.04445.x
![]() |
[53] | An L, Linderman M, Qi J, et al. (2005) Exploring complexity in a human-environment system: An agent-based spatial model for multidisciplinary and multiscale integration. Ann Assoc American Geogr 95, 54-79. |
[54] |
Miller BW, Breckheimer I, McCleary AL, et al. (2010) Using stylized agent-based models for population-environment research: a case study from the Galápagos Islands. Popul Environ 31:401-426. doi: 10.1007/s11111-010-0110-4
![]() |
[55] |
Frid L, Hanna D, Korb N, et al. (2013) Evaluating alternative weed management strategies for three Montana landscapes. Invasive Plant Sci Manage 6: 48-59. doi: 10.1614/IPSM-D-11-00054.1
![]() |
[56] |
Frid L, Holcombe T, Morisette JT, et al. (2013) Using state-and-transition modeling to account for imperfect detection in invasive species management. Invasive Plant Sci Manage 6: 36-47. doi: 10.1614/IPSM-D-11-00065.1
![]() |
[57] | Windrum P, Fagiolo G, Moneta A (2007) Empirical validation of agent-based models: Alternatives and prospects. J Artif Soc Soc Simulat 10: 8. |
[58] |
Oreskes N, Shrader-Frechette K, Belitz K (1994) Verification, validation, and confirmation of numerical models in the earth sciences. Science 263: 641-646. doi: 10.1126/science.263.5147.641
![]() |
[59] |
Oreskes N (1998) Evaluation (not validation) of quantitative models. Environmental Health Perspectives 106: 1453-1460. doi: 10.1289/ehp.98106s61453
![]() |
[60] |
Grimm V, Revilla E, Berger U, et al. (2005) Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science 310: 987-991. doi: 10.1126/science.1116681
![]() |
[61] | Jørgensen SE (1986) Fundamentals of Ecological Modelling. Amsterdam: Elsevier. |
[62] | Macfarlane WW, Logan JA, Kern WR (2009) Using the landscape assessment system (LAS) to assess mountain pine beetle-caused mortality of whitebark pine, Greater Yellowstone Ecosystem, 2009. Project report prepared for the Greater Yellowstone Coordinating Committee, Whitebark Pine Subcommittee, Jackson, WY. |
[63] | Shanahan E, Irvine KM, Roberts D, et al. (2014) Status of whitebark pine in the Greater Yellowstone Ecosystem: A step-trend analysis comparing 2004-2007 to 2008- 2011. Natural Resource Technical Report NPS/GRYN/NRTR—2014/917. Fort Collins, CO: National Park Service. |
[64] |
Régnière J, Bentz BJ (2007) Modeling cold tolerance in the mountain pine beetle, Dendroctonus pondersoae. J Insect Physiol 53: 559-572. doi: 10.1016/j.jinsphys.2007.02.007
![]() |
[65] |
Logan JA, Macfarlane WW, Wilcox L (2010) Whitebark pine vulnerability to climate-driven mountain pine beetle disturbance in the Greater Yellowstone Ecosystem. Ecol Appl 20: 895-902. doi: 10.1890/09-0655.1
![]() |
[66] | Keane RE (2001) Successional dynamics: Modeling an anthropogenic threat, In: Tomback DF, Arno SF, Keane RE, editors, Whitebark Pine Communities: Ecology and Restoration. Washington, DC: Island Press, 159-192. |
[67] | Whitlock C, Millspaugh, SH (2001) A paleoecologic perspective on past plant invasions in Yellowstone. West N Am Naturalist 61: 316-327. |
[68] | LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) dataset. Available online at www.landfire.gov. |
[69] | Teste FP, Lieffers VJ, Landhausser SM (2011) Viability of forest floor and canopy seed banks in Pinus contorta var. latifoia (Pinaceas) forests after a mountain pine beetle outbreak. American Journal of Botany 98: 630-637. |
[70] | MTBS (Monitoring Trends in Burn Severity) dataset. Available from: at www.mtbs.gov. |
[71] | USFS (United States Forest Service), Species life history information. Available from: www.fs.fed.us. |
[72] |
Johnson EA, Fryer GI (1996) Why Engelmann spruce does not have a persistent seed bank. Canadian Journal of Forest Research, 26: 872-878 doi: 10.1139/x26-095
![]() |
[73] | Hatala JA, Crabtree, RL (2009) Modelling the spread of blister rust in the Greater Yellowstone Ecosystem. Nutcracker Notes, Whitebark Pine Ecosystem Foundation 16: 17-18. |
1. | Ioannis Karaouzas, Natalia Kapetanaki, Angeliki Mentzafou, Theodore D. Kanellopoulos, Nikolaos Skoulikidis, Heavy metal contamination status in Greek surface waters: A review with application and evaluation of pollution indices, 2021, 263, 00456535, 128192, 10.1016/j.chemosphere.2020.128192 |
Site | No of sampling locations (no of samples) | Parameters analyzed | Methods | Labs (*) |
HAI | 1 (1) | Cr, Ni, Cu, Zn, Pb, Al | Digestion with AR(a) | Andreou |
Cr(Ⅵ) | Elution with water(b) | |||
Europa | 6 (6) | Cr, Ni | Digestion with AR(a) | Andreou |
Cr(Ⅵ) | Elution with water(b) | |||
Aluminco | 1 (1) | Cr, Ni, Fe, Al | Digestion with AR(a)XRF(d) | EuF/LabMet |
Cr(Ⅵ) | Alkaline digestion(c) | LabMet | ||
Viometale | 1 (1) | Cr, Ni, Cu, Zn, etc.Cr(Ⅵ) | XRF(d), AR(a)Alkaline digestion(c) | LabMet |
Site | No of sampling locations (no of samples) | Parameters analyzed | Methods | Labs (*) | |
Un-contaminated | Suspected for contamination | ||||
HAI | 1 (7) | 3 (42) | Cr, Ni, Cu, Zn, Pb, Al | Digestion with AR(a) | Andreou |
Cr(Ⅵ) | Elution with water(b) | ||||
Europa | 6 (13) | 4 (49) | Cr, Ni | Digestion with AR(a) | Andreou |
Cr(Ⅵ) | Elution with water(b) | ||||
Aluminco | 1 (6) | 4 (12) | Cr, Ni, Fe, Al | Digestion with AR(a)XRF(d) | EuF/LabMet |
Cr(Ⅵ) | Alkaline digestion(c) | LabMet | |||
Viometale | 1 (4) | 6 (19) | Cr, Ni, Cu, Zn, etc. | XRF(d)AR(a) | LabMet |
Cr(Ⅵ) | Alkaline digestion(c) |
Site | No of sampling locations (no of samples) | Parameters analyzed | Methods | Labs (*) | |
Un-contaminated | Suspected for contamination | ||||
Europa | 2 | 3 | Cr, Ni, Fe, Al | Digestion with AR(a) | LabMet |
Cr(Ⅵ) | Alkaline digestion(c) | ||||
Aluminco | 1 | 5 | Cr, Ni, Fe, Al | Digestion with AR(a) | LabMet |
Cr(Ⅵ) | Alkaline digestion(c) |
Site | No of sampling locations (no of samples) | Parameters analyzed |
Methods | Labs(*) | |
Un-contaminated | Suspected for contamination | ||||
Boreholes | - | 38 | Cr, Ni, Fe, Al | XRF(d) | LabMet |
Cr(Ⅵ) | AR(a) | ||||
Surface Soil | - | 12 | Cr, Ni, Fe, Al | XRF(d) | LabMet |
Cr(Ⅵ) | AR(a) | ||||
(a) Digestion with aqua regia followed by determination of metals in solution by AAS or ICP-MS (EN 13657) (b) Elution with water, determination of soluble Cr(Ⅵ) (DIN 38405-24: 05.87, AWWA-3500-Cr/B) (c) Alkaline digestion, determination of extracted Cr(Ⅵ) (USEPA, SW-846 Methods 3060A and 7196) (d) Determination of total elements concentration by X-ray fluorescence spectrometry (EN 15309) (*) Laboratories: (a) Andreou, K. Andreou. Ltd, Athens, (b) EuF: Eurofins Umwelt Ost GmbH, Jena, Germany, (c) LabMet: Laboratory of Metallurgy, NTUA, Athens. For the majority of samples, namely those collected from HAI, Europa and Aluminco, the elemental analysis was carried out following the digestion of samples with aqua regia (AR). The samples collected from Viometale were analyzed by X-ray fluorescence (XRF) spectrometry, (mainly due to time constraints - XRF analysis is much more rapid, as there is no need for any pretreatment steps, such as acid leaching or fusion). The LIFE-CHARM samples were also analyzed by XRF. |
Area (Number of samples) |
Cr (mg/kg) |
Ni (mg/kg) |
Cr(Ⅵ) (mg/kg) |
Source | ||
Range | Mean | Range | Mean | Range | ||
Asopos (n = 30) |
60-410 | 220 | 91-1200 | 620 | >0.1-9.3 (a) | [21] |
Oropos (n = 33) |
17-600 | 212 | -- | [3] | ||
Thebes (n = 51) |
134-856 | 277 | 621-2639 | 1591 | -- | [23] |
Atalante (n = 64) |
48-4200 | 453 | 44-2730 | 533 | -- | [24] |
All Greece (n = 41) |
2-466 | 102 | 2-1812 | 171 | -- | [22] |
(a) Cr(Ⅵ) detected in 3 among the 30 analyzed reference soils (5.5, 6.0 and 9.3 mg/kg). |
Soil limit values (mg/kg) | ||||||
Residential areas* | Industrial areas* | |||||
IT | DE | BE(W) | IT | DE | BE(W) | |
Cr | 150 | 400 | 520 | 800 | 1000 | 700 |
Ni | 120 | 140 | 300 | 500 | 900 | 500 |
Cr(Ⅵ) | -- | -- | 4.2 | -- | -- | -- |
(*) Cr, Ni digestion of samples with aqua regia (AR). |
Site | No of sampling locations (no of samples) | Parameters analyzed | Methods | Labs (*) |
HAI | 1 (1) | Cr, Ni, Cu, Zn, Pb, Al | Digestion with AR(a) | Andreou |
Cr(Ⅵ) | Elution with water(b) | |||
Europa | 6 (6) | Cr, Ni | Digestion with AR(a) | Andreou |
Cr(Ⅵ) | Elution with water(b) | |||
Aluminco | 1 (1) | Cr, Ni, Fe, Al | Digestion with AR(a)XRF(d) | EuF/LabMet |
Cr(Ⅵ) | Alkaline digestion(c) | LabMet | ||
Viometale | 1 (1) | Cr, Ni, Cu, Zn, etc.Cr(Ⅵ) | XRF(d), AR(a)Alkaline digestion(c) | LabMet |
Site | No of sampling locations (no of samples) | Parameters analyzed | Methods | Labs (*) | |
Un-contaminated | Suspected for contamination | ||||
HAI | 1 (7) | 3 (42) | Cr, Ni, Cu, Zn, Pb, Al | Digestion with AR(a) | Andreou |
Cr(Ⅵ) | Elution with water(b) | ||||
Europa | 6 (13) | 4 (49) | Cr, Ni | Digestion with AR(a) | Andreou |
Cr(Ⅵ) | Elution with water(b) | ||||
Aluminco | 1 (6) | 4 (12) | Cr, Ni, Fe, Al | Digestion with AR(a)XRF(d) | EuF/LabMet |
Cr(Ⅵ) | Alkaline digestion(c) | LabMet | |||
Viometale | 1 (4) | 6 (19) | Cr, Ni, Cu, Zn, etc. | XRF(d)AR(a) | LabMet |
Cr(Ⅵ) | Alkaline digestion(c) |
Site | No of sampling locations (no of samples) | Parameters analyzed | Methods | Labs (*) | |
Un-contaminated | Suspected for contamination | ||||
Europa | 2 | 3 | Cr, Ni, Fe, Al | Digestion with AR(a) | LabMet |
Cr(Ⅵ) | Alkaline digestion(c) | ||||
Aluminco | 1 | 5 | Cr, Ni, Fe, Al | Digestion with AR(a) | LabMet |
Cr(Ⅵ) | Alkaline digestion(c) |
Site | No of sampling locations (no of samples) | Parameters analyzed |
Methods | Labs(*) | |
Un-contaminated | Suspected for contamination | ||||
Boreholes | - | 38 | Cr, Ni, Fe, Al | XRF(d) | LabMet |
Cr(Ⅵ) | AR(a) | ||||
Surface Soil | - | 12 | Cr, Ni, Fe, Al | XRF(d) | LabMet |
Cr(Ⅵ) | AR(a) | ||||
(a) Digestion with aqua regia followed by determination of metals in solution by AAS or ICP-MS (EN 13657) (b) Elution with water, determination of soluble Cr(Ⅵ) (DIN 38405-24: 05.87, AWWA-3500-Cr/B) (c) Alkaline digestion, determination of extracted Cr(Ⅵ) (USEPA, SW-846 Methods 3060A and 7196) (d) Determination of total elements concentration by X-ray fluorescence spectrometry (EN 15309) (*) Laboratories: (a) Andreou, K. Andreou. Ltd, Athens, (b) EuF: Eurofins Umwelt Ost GmbH, Jena, Germany, (c) LabMet: Laboratory of Metallurgy, NTUA, Athens. For the majority of samples, namely those collected from HAI, Europa and Aluminco, the elemental analysis was carried out following the digestion of samples with aqua regia (AR). The samples collected from Viometale were analyzed by X-ray fluorescence (XRF) spectrometry, (mainly due to time constraints - XRF analysis is much more rapid, as there is no need for any pretreatment steps, such as acid leaching or fusion). The LIFE-CHARM samples were also analyzed by XRF. |
Area (Number of samples) |
Cr (mg/kg) |
Ni (mg/kg) |
Cr(Ⅵ) (mg/kg) |
Source | ||
Range | Mean | Range | Mean | Range | ||
Asopos (n = 30) |
60-410 | 220 | 91-1200 | 620 | >0.1-9.3 (a) | [21] |
Oropos (n = 33) |
17-600 | 212 | -- | [3] | ||
Thebes (n = 51) |
134-856 | 277 | 621-2639 | 1591 | -- | [23] |
Atalante (n = 64) |
48-4200 | 453 | 44-2730 | 533 | -- | [24] |
All Greece (n = 41) |
2-466 | 102 | 2-1812 | 171 | -- | [22] |
(a) Cr(Ⅵ) detected in 3 among the 30 analyzed reference soils (5.5, 6.0 and 9.3 mg/kg). |
Soil limit values (mg/kg) | ||||||
Residential areas* | Industrial areas* | |||||
IT | DE | BE(W) | IT | DE | BE(W) | |
Cr | 150 | 400 | 520 | 800 | 1000 | 700 |
Ni | 120 | 140 | 300 | 500 | 900 | 500 |
Cr(Ⅵ) | -- | -- | 4.2 | -- | -- | -- |
(*) Cr, Ni digestion of samples with aqua regia (AR). |